Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

Neural information extraction algorithms can be trained on data from a few institutions and then can be applied to data from previously unseen hospitals and clinics. This helps to reduce the burden ...

By using a neural network-based decoupling algorithm, the team was able to resolve spectral interference within the existing system, reducing both the complexity and cost of the design.

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

The algorithm uses supervised learning with known histopathology diagnoses (malignant and nonmalignant) as the labels for algorithm training. MIA3G is a classification deep feedforward neural network ...

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data.

Artificial neural networks, the underlying structure of deep learning algorithms, roughly mimic the physical structure of the human brain.